An improved spectral clustering method for community detection under the degree-corrected stochastic blockmodel
This work addresses community detection in networks, offering an incremental improvement for researchers in network analysis.
The authors tackled the community detection problem in networks by proposing an improved spectral clustering (ISC) method under the degree-corrected stochastic block model, which outperformed classical spectral clustering methods on simulated and empirical networks, achieving error rates of 121/1137 and 96/590 on two weak signal networks.
For community detection problem, spectral clustering is a widely used method for detecting clusters in networks. In this paper, we propose an improved spectral clustering (ISC) approach under the degree corrected stochastic block model (DCSBM). ISC is designed based on the k-means clustering algorithm on the weighted leading K + 1 eigenvectors of a regularized Laplacian matrix where the weights are their corresponding eigenvalues. Theoretical analysis of ISC shows that under mild conditions the ISC yields stable consistent community detection. Numerical results show that ISC outperforms classical spectral clustering methods for community detection on both simulated and eight empirical networks. Especially, ISC provides a significant improvement on two weak signal networks Simmons and Caltech, with error rates of 121/1137 and 96/590, respectively.